Though the objectiveness in (REF ) is a joint optimization of temporally seperated tasks \(\mathcal {L}(f(\mathbf {x};\theta ), \mathbf {y}), (\mathbf {x}, \mathbf {y})\in \mathcal {D}^{t}\) , continual neural mapping can be understood from the perspectives of four relevant learning paradigms as illustrated in Fig. REF . Clarifying our problem setting from the most relevant continual learning perspective, continual neural mapping falls into a domain-incremental [1]} continual learning scenario: we aim to maintain a globally consistent representation with a single network from sequential data, where data distribution shifts and the objective remains the same.
